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Exploring the Neural Correlates of Cognitive Reappraisal in Obsessive-Compulsive Disorder Using Task-based Functional Magnetic Resonance Imaging

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An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI.

Nathan W Churchill¹, Robyn Spring², Babak Afshin-Pour³

¹Rotman Research Institute, Baycrest Hospital, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.

|July 11, 2015

Summary

This summary is machine-generated.

Optimizing preprocessing pipelines for blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) significantly enhances data reliability and prediction accuracy. This adaptive framework improves results even with brief scanning sessions, enabling robust brain-behavior correlations.

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Area of Science:

Neuroimaging
Cognitive Neuroscience
Data Science

Background:

Blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) measures brain activity but suffers from weak signals and noise.
Preprocessing pipelines critically influence fMRI signal detection, yet are seldom quantitatively validated due to complex interactions.

Purpose of the Study:

To introduce and validate an adaptive resampling framework for optimizing fMRI preprocessing pipelines.
To enhance data-driven metrics of task prediction and spatial reproducibility in fMRI analysis.

Main Methods:

Developed an adaptive resampling framework to evaluate and optimize preprocessing choices.
Optimized pipelines using data-driven metrics for task prediction and spatial reproducibility.
Validated the framework against standard "fixed" preprocessing pipelines.

Main Results:

The adaptive optimization approach significantly improved within-subject test-retest reliability and between-subject activation overlap.
Behavioral prediction accuracy was substantially enhanced compared to standard pipelines.
Improvements were consistent across various experimental tasks and analysis models, even with short scan times (<3 minutes).

Conclusions:

Preprocessing choices act as implicit model regularizers in fMRI analysis.
Pipeline optimization yields reliable results and brain-behavior correlations in smaller datasets.
The adaptive framework offers a robust method for validating and optimizing fMRI preprocessing strategies.